Speeding Up Network Layout and Centrality Measures for Social Computing Goals

This paper presents strategies for speeding up calculation of graph metrics and layout by exploiting the parallel architecture of modern day Graphics Processing Units (GPU), specifically Compute Unified Device Architecture (CUDA) by Nvidia. Graph centrality metrics like Eigenvector, Betweenness, Page Rank and layout algorithms like Fruchterman-Rheingold are essential components of Social Network Analysis (SNA). With the growth in adoption of SNA in different domains and increasing availability of huge networked datasets for analysis, social network analysts require faster tools that are also scalable. Our results, using NodeXL, show up to 802 times speedup for a Fruchterman-Rheingold graph layout and up to 17,972 times speedup for Eigenvector centrality metric calculations on a 240 core CUDA-capable GPU.